Radar image inversion implements techniques of processing that are largely nonlinear. There are infinite number of solutions due to limited (sparse) visibility data (large areas where sampling function is zero), and errors in the measurements themselves. The limited visibility plane coverage (sparse baselines) can be improved by deconvolution processes that allow the unmeasured visibility to take nonzero values within some general constraints on the image. There are two major methods to solve the under-determined problem of image inversion: The CLEAN algorithm and Maximum Entropy Method (MEM). Other techniques, like Capon and Non-Negative Least Squares (NNLS), were also considered during Task 10.2 of the EISCAT_3D Preparatory Phase.

The MEM method was determined to be the preferred one since it is most mathematically developed and works well with broad, smooth brightness distributions. In case of presence of point-like sources MEM and CLEAN methods should be combined. The Capon algorithm should be used in case of fast moving targets like satellites or space debris.

Finishing this comparison and ranking corresponds to Milestone 10.6 of the project, and this Milestone was reached in May 2012.